Abstract
Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.
Highlights
Final drive is the core component in the rear axle of automobile and is always running under complex operating conditions and inevitably faults
A novel diagnostic model based on multiple deep neural network (DNN) (MDNNs) and softmax is constructed with the input of fused deep features
A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented
Summary
Final drive is the core component in the rear axle of automobile and is always running under complex operating conditions and inevitably faults. Any fault of final drive may cause severe human injury, production stoppage and economic loss [1,2]. The change of status is hidden in the vibrational characteristics produced during the operating period. Fault diagnosis with vibration signal is extremely efficient in status detection and failure recognition of machinery [3,4,5,6,7,8]. P.K. Wong used Ensemble Empirical Mode Decomposition (EEMD) in the failure recognition of gears [9]
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